Episodic Detection of Spoofed Data In Synchrophasor Measurement Streams

Published In

2019 Tenth International Green and Sustainable Computing Conference (IGSC)

Document Type

Conference Proceeding

Publication Date



Spoofed Data


Phasor Measurement Units (PMUs) provide high-quality state information about the electrical grid in near-real time. However, as utilities become more reliant on these measurements, the devices themselves as well as the communication network that supports them will likely become a more prominent attack surface for cyber threats. In this paper, we demonstrate a system designed to find anomalous PMU data-specifically data that is intended to provide false signal readings (spoofed data) over a period of time. Our system uses support vector machines to distinguish between “normal” system operation and “spoofed” operation. The work presented here makes three main contributions. Specifically, we demonstrate: (1) a SVM-based classifier that has reasonable longevity (i.e., once trained, the classifier remains valid for a reasonable length of time); (2) a distributed version of our classifier that improves the efficiency and scalability; and (3) the classifiers above can be used to detect spoofs at different levels of fidelity which can have a dramatic effect on their suitability in a real-world operating environment.


Copyright (2019) IEEE

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